我们提出了邻居一致性正则化(Neighbor Consistency Regularization),这是一种新的损失项,用于深度学习,带有噪声标签,鼓励具有相似特征表示的示例具有相似的预测。 我们通过经验验证,在合成和真实分布的大范围噪声水平下,NCR比几个重要基线获得了更好的精度,并且是混合[48]的流行正则化技术的补充。 我们证明,NCR在评估合...
Building upon CIFAR datasets, this paper provide the weakly supervised learning community with two accessible and easy-to-use benchmarks: CIFAR-10N and CIFAR-100N. it introduce new observations from human annotations such as imbalanced annotations, the flipping of noisy labels among similar features,...
通过prune, count, rank 3 步可以高效率算出 joint probabilities(true and predicted labels) 根据joint probabilities 识别 label error。 理论基础是 Angluin 1988 的 CNP 理论。 本文的核心贡献: we prove CL exactly estimates the ...
原文链接:凤⭐尘 》》https://www.cnblogs.com/phoenixash/p/15369008.html 基本信息 \1.标题:DIVIDEMIX: LEARNING WITH NOISY LABELS AS SEMI-SUPERVISED LEARNING \2.作者:Junnan Li, Richard Socher, Steven C.H. Hoi \3.作者单位:Salesforce Research \4.发表期刊/会议:ICLR \5.发表时间:2020 \6.原...
Learning with noisy labels means When we say "noisy labels," we mean that an adversary has intentionally messed up the labels, which would have come from a "clean" distribution otherwise. This setting can also be used to cast learning from only positive and unlabeled data....
Machine Learning and Noisy Labels: Definitions, Theory, Techniques and Solutions provides an ideal introduction to machine learning with noisy labels that is suitable for senior un ... read full description Purchase book Share this bookBrowse content ...
Noise may be modelled explicitly by modelling "label flips", where incorrect binary labels are "flipped" relative to their ground truth value. Distributions of label flips may be modelled as prior and posterior distributions in a flexible architecture for machine learning systems. An arbitrary ...
2.1 LEARNING WITH NOISY LABELS 现有的训练带噪声标签的dnn的方法大都是为了修正loss函数。修正方法可以分为两类。第一种方法对所有样本一视同仁,通过重新标记噪声样本来显式或隐式地纠正损失。对于重标记方法,对噪声样本的建模采用有向图模型(Xiao et al., 2015)、条件随机场(Vahdat, 2017)、知识图(Li et al...
Improved Mix-up with KL-Entropy for Learning From Noisy Labels In this work, we propose an improved joint optimization framework, which mixed the mix-up entropy and Kullback-Leibler (KL) entropy as the loss function. The new loss function can give the better fine-tuning after the framework ...
This repository is the official implementation ofAsymmetric Loss Functions for Learning with Noisy Labels[ICML 2021] andAsymmetric Loss Functions for Noise-tolerant Learning: Theory and Applications[T-PAMI]. Requirements Python >= 3.6, PyTorch >= 1.3.1, torchvision >= 0.4.1, numpy>=1.11.2, tqdm...